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Exit, Tweets, and Loyalty

Author

Listed:
  • Joshua S. Gans
  • Avi Goldfarb
  • Mara Lederman

Abstract

Hirschman's Exit, Voice, and Loyalty highlights the role of "voice" when individuals confront an unexpected deterioration in quality. Yet, voice has received little attention. To motivate our empirical analysis, we develop a simple model of voice as the equilibrium of a relational contract between customers and firms. We use data on 4 million tweets to or about US airlines to study the relationship between quality, voice, and market structure. Voice increases when quality deteriorates. This relationship is greater for airlines that operate a large share of flights in a market. Supplemental analyses support a relational contracting role for voice.

Suggested Citation

  • Joshua S. Gans & Avi Goldfarb & Mara Lederman, 2021. "Exit, Tweets, and Loyalty," American Economic Journal: Microeconomics, American Economic Association, vol. 13(2), pages 68-112, May.
    • Handle: RePEc:aea:aejmic:v:13:y:2021:i:2:p:68-112
    DOI: 10.1257/mic.20180085
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    Cited by:

    1. Sunghun Chung & Animesh Animesh & Kunsoo Han & Alain Pinsonneault, 2020. "Financial Returns to Firms’ Communication Actions on Firm-Initiated Social Media: Evidence from Facebook Business Pages," Information Systems Research, INFORMS, vol. 31(1), pages 258-285, March.
    2. Ramah Al Balawi & Yuheng Hu & Liangfei Qiu, 2023. "Brand Crisis and Customer Relationship Management on Social Media: Evidence from a Natural Experiment from the Airline Industry," Information Systems Research, INFORMS, vol. 34(2), pages 442-462, June.
    3. Achyuta Adhvaryu & Teresa Molina & Anant Nyshadham, 2019. "Expectations, Wage Hikes, and Worker Voice: Evidence from a Field Experiment," NBER Working Papers 25866, National Bureau of Economic Research, Inc.
    4. Behrens, Christiaan & de Jong, Gerben & van Ommeren, Jos, 2024. "From silver to platinum: The impact of frequent flier tier levels on air travellers’ behaviour," Transportation Research Part B: Methodological, Elsevier, vol. 186(C).
    5. Mara Lederman, 2018. "Comment on "The Impact of Machine Learning on Economics"," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 548-551, National Bureau of Economic Research, Inc.
    6. Daniel Levy & Andrew T. Young, 2021. "Promise, trust, and betrayal: Costs of breaching an implicit contract," Southern Economic Journal, John Wiley & Sons, vol. 87(3), pages 1031-1051, January.
    7. Agata Skorupka, 2024. "Detecting anomalies in graph networks on digital markets," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-30, December.
    8. Michele Fioretti & Victor Saint-Jean & Simon C. Smith, 2021. "The Shared Costs of Pursuing Shareholder Values," Papers 2103.12138, arXiv.org, revised Feb 2026.
    9. Bertschek, Irene & Kesler, Reinhold, 2022. "Let the user speak: Is feedback on Facebook a source of firms’ innovation?," Information Economics and Policy, Elsevier, vol. 60(C).
    10. Judith A. Chevalier & Yaniv Dover & Dina MayzlinDina Mayzlin, 2018. "Channels of Impact: User Reviews When Quality Is Dynamic and Managers Respond," Marketing Science, INFORMS, vol. 37(5), pages 688-709, September.
    11. Devesh Raval, 2020. "Whose Voice Do We Hear in the Marketplace? Evidence from Consumer Complaining Behavior," Marketing Science, INFORMS, vol. 39(1), pages 168-187, January.

    More about this item

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L15 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Information and Product Quality
    • L82 - Industrial Organization - - Industry Studies: Services - - - Entertainment; Media
    • L93 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Air Transportation

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